Dynamic moral hazard with learning about the production function

• Ano de defesa: 2014
• Autor(a) principal: Matsumoto, Maurício Massao Soares
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Aprendizagem; Contracts; Contratos; Incentives; Incentivos; Learning; Linear programming; Programação linear
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/12/12138/tde-29092014-165855/

In this work we propose a flexible numerical approach to deal with models of dynamic moral hazard with simultaneous learning about the production function. Because of the complexity of the problem, analytical solutions have so far been limited in scope. The contribution is methodological: through computation, the problem can be studied under few assumptions about functional forms. We depart from a general mechanism, reformulate it as an incentive compatible mechanism, and show how it can be solved by backward induction through a sequence of linear programs. We apply our method to a few cases of interest, and confirm that uncertainty about the production function increases the volatility of the agent\'s utility in order to prevent belief manipulation, as found in the literature.